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Affective brain-computer interfaces (aBCIs) have garnered widespread applications, with remarkable advancements in utilizing electroencephalogram (EEG) technology for emotion recognition. However, the time-consuming process of annotating EEG data, inherent individual differences, non-stationary characteristics of EEG data, and noise artifacts in EEG data collection pose formidable challenges in developing subject-specific cross-session emotion recognition models. To simultaneously address these challenges, we propose a unified pre-training framework based on multi-scale masked autoencoders (MSMAE), which utilizes large-scale unlabeled EEG signals from multiple subjects and sessions to extract noise-robust, subject-invariant, and temporal-invariant features. We subsequently fine-tune the obtained generalized features with only a small amount of labeled data from a specific subject for personalization and enable cross-session emotion recognition. Our framework emphasizes: 1) Multi-scale representation to capture diverse aspects of EEG signals, obtaining comprehensive information; 2) An improved masking mechanism for robust channel-level representation learning, addressing missing channel issues while preserving inter-channel relationships; and 3) Invariance learning for regional correlations in spatial-level representation, minimizing inter-subject and inter-session variances. Under these elaborate designs, the proposed MSMAE exhibits a remarkable ability to decode emotional states from a different session of EEG data during the testing phase. Extensive experiments conducted on the two publicly available datasets, i.e., SEED and SEED-IV, demonstrate that the proposed MSMAE consistently achieves stable results and outperforms competitive baseline methods in cross-session emotion recognition.

Kiasu-BC and Joltik-BC are internal tweakable block ciphers of authenticated encryption algorithms Kiasu and Joltik submitted to the CAESAR competition. Kiasu-BC is a 128-bit block cipher, of which tweak and key sizes are 64 and 128 bits, respectively. Joltik-BC-128 is a 64-bit lightweight block cipher supporting 128 bits tweakey. Its designers recommended the key and tweak sizes are both 64 bits. In this paper, we propose improved meet-in-the-middle attacks on 8-round Kiasu-BC, 9-round and 10-round Joltik-BC-128 by exploiting properties of their structures and using precomputation tables and the differential enumeration. For Kiasu-BC, we build a 5-round distinguisher to attack 8-round Kiasu-BC with |$2^{109}$| plaintext–tweaks, |$2^{112.8}$| encrytions and |$2^{92.91}$| blocks. Compared with previously best known cryptanalytic results on 8-round Kiasu-BC under chosen plaintext attacks, the data and time complexities are reduced by |$2^{7}$| and |$2^{3.2}$| times, respectively. For the recommended version of Joltik-BC-128, we construct a 6-round distinguisher to attack 9-round Joltik-BC-128 with |$2^{53}$| plaintext–tweaks, |$2^{56.6}$| encryptions and |$2^{52.91}$| blocks, respectively. Compared with previously best known results, the data and time complexities are reduced by |$2^7$| and |$2^{5.1}$| times, respectively. In addition, we present a 6.5-round distinguisher to attack 10-round Joltik-BC-128 with |$2^{53}$| plaintext–tweaks, |$2^{101.4}$| encryptions and |$2^{76.91}$| blocks. [ABSTRACT FROM AUTHOR]

With the rapid development of information technologies, more and more data are collected from multiple sources, which contain different perspectives of the data. To accurately explore the shared information among multiple views, K-means based multi-view clustering methods are designed and widely used in various applications for their simplicity and efficiency. However, all of these methods cluster data in the original high-dimensional feature space which is extremely time-consuming and sensitive to outliers, or cluster data in the embedded feature space for each view, which is hard to find the optimal reduced dimensionality. To solve these problems, we propose a robust discriminative multi-view K-means clustering with feature selection and group sparsity learning. Compared to the state-of-the-arts, the proposed algorithm has two advantages: 1) Discriminative K-means clustering and feature learning are integrated jointly into a single framework, where robust and accurate clustering results are obtained in the embedded feature space with an l2, 1-norm based loss function. 2) Group sparsity constraints are imposed to select the most relevant features and the most important views. We apply the proposed algorithm to serval kinds of multimedia understanding applications. Experimental results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• A target vector replacement strategy (TVRS) is proposed. • Various differential evolution algorithms can be improved by TVRS. • The TVRS can be easily applied to the existing differential evolution algorithms. Individuals find it difficult to avoid stagnation in the iterative process of the differential evolution (DE) algorithm, and the stagnant individuals have restricted improvement in the population, which will have a negative effect on its performance. This research put forward a target vector replacement strategy (TVRS) for reducing the impact of stagnant individuals on the DE algorithm's performance. For stagnant target vectors, while executing mutation operation and crossover operation, TVRS selects non-stagnant individuals in the population to replace the stagnant target vectors with a specific probability. Because improving stagnant individuals are difficult, TVRS provides more opportunities for improvement to those who are not stagnant, while the opportunities for stagnant individuals to be improved are decreased. For assessing the efficiency of TVRS, TVRS was implemented to six DE algorithms and compared to their original algorithms. Judging from the performance outcomes of TVRS in the CEC 2014 benchmark test set, TVRS can greatly increase the DE algorithm's performance. This study will show the evolutionary opportunity that is obtained by an individual should be related to the individual state. Adjusting the individual evolutionary opportunity based on the individual state is helpful for improving the stagnation problem of the differential evolution algorithm. This study will provide a new way for improving the stagnation problem of the differential evolution algorithm and other evolutionary algorithms. [ABSTRACT FROM AUTHOR]

Most research on improving differential evolution algorithms has focused on mutation operator and parameter control. In this paper, a new selection operator is proposed to improve differential evolution algorithm performance. When the individual is not in a state of stagnation, the proposed selection operator is the same as the classical selection operator, meaning that it chooses the best vector from the trial vector and parent vector to survive to the next generation. When the individual is in a state of stagnation, the three other candidate vectors may survive to the next generation. The first candidate vector is the best vector of all the discarded trial vectors of the parent vector. The second candidate vector is the second-best vector of all the discarded trial vectors of the parent vector. The third candidate vector is randomly chosen from all the successfully updated solutions. The proposed selection operator will improve the differential evolution algorithm's ability to escape the local optimal value. 58 benchmark functions are used for verification of the proposed selection operator's performance. Experiments were conducted in order to compare six differential evolution algorithms' performances using the proposed selection operator and not using the proposed selection operator. Simulation results showed that the proposed selection operator significantly improved the differential evolution algorithm's performance. • A new selection operator is proposed for differential evolution (DE). • Various DEs can be improved by the new selection operator. • The new selection operator can be easily applied to the existing DEs. [ABSTRACT FROM AUTHOR]